1 The Data

In this analysis, we will be looking at USA Today articles published during 2010-2019. Using Nexis Uni, articles were pulled with the following search criteria in the headline or leading paragraph of the article:

  • climate change
  • global warming
  • climate crisis

After pulling this, we found a total of 517 articles on climate change across the ten-year period, as follows.

Year Articles
2010 36
2011 38
2012 29
2013 66
2014 69
2015 99
2016 43
2017 59
2018 28
2019 48

1.1 Word Cloud

Aggregating all words in these articles together, we find the following as the top fifteen most frequent words used.

word count
climate 2474
change 1655
global 944
warming 815
energy 755
carbon 635
u.s 585
president 570
emissions 564
people 556
obama 522
gas 484
world 475
report 462
power 435

We can visualize this as a word cloud!


If you have trouble following that one, here’s one more your style #yeehaww!!!



2 Sentiment Analysis

To best analyze the tone used in the USA Today articles, we break the data up by year and look at the various sentiments used in the texts; we do so with the following lexicons:

  • 2015 Lexicoder Sentiment Dictionary
  • NRC Lexicon

2.1 2015 Lexicoder Sentiment Dictionary

In analyzing political texts, the 2015 Lexicoder Sentiment Dictionary was used to analyze sentiment glob-style, returning positive and negative sentiment in context of the sentence. For the USA Today articles in total we see the following

Score
Positive 11142
Negative 11234
Sentiment -92

With an overall sentiment score of -92 across all articles, USA Today stays almost perfectly equal in positive and negative verbage. This will be interesting to compare across networks in further research.

Subtracting the negative score from the positive score gives the complete sentiment value. Calculating the sentiment value for each year we find the following:

2.2 NRC Lexicon

According to Saif Mohammad and Peter Turney, the NRC Emotion Lexicon associate each word in the English language with eight basic emotions plus positive/negstive sentiment.

  • anger
  • fear
  • anticipation
  • trust
  • surprise
  • sadness
  • joy
  • disgust
  • negative sentiment
  • positive sentiment

In analyzing all the texts together we see the following distribution of these eight attributes.

Further breaking it down by year, we see trends in the emotions over time as follows:

Here is the same data with a line graph.

NOTE: The data is not scaled in this analysis.

3 Bibliography

Mohammad, Saif M. (2016). The Sentiment and Emotion Lexicons. National Research Council of Canada.

Young, L. & Soroka, S. (2012). Affective News: The Automated Coding of Sentiment in Political Texts. Political Communication, 29(2), 205–231.